{"doi":"10.1016/j.mcpro.2025.100968","title":"TMT-Based Multiplexed (Chemo)Proteomics on the Orbitrap Astral Mass Spectrometer","abstract":"Ongoing advancements in instrumentation has established mass spectrometry (MS) as an essential tool in proteomics research and drug discovery. The newly released Asymmetric Track Lossless (Astral) analyzer represents a major step forward in MS instrumentation. Here, we evaluate the Orbitrap Astral mass spectrometer in the context of tandem mass tag (TMT)-based multiplexed proteomics and activity-based proteome profiling, highlighting its sensitivity boost relative to the Orbitrap Tribrid platform-50% at the peptide and 20% at the protein level. We compare TMT data-dependent acquisition and label-free data-independent acquisition on the same instrument, both of which quantify over 10,000 human proteins per sample within 1 h. TMT offers higher quantitative precision and data completeness, while data-independent acquisition is free of ratio compression and is thereby more accurate. Our results suggest that ratio compression is prevalent with the high-resolution MS2-based quantification on the Astral, while real-time search-based MS3 quantification on the Orbitrap Tribrid platform effectively restores accuracy. Additionally, we benchmark TMT-based activity-based proteome profiling by interrogating cysteine ligandability. The Astral measures over 30,000 cysteines in a single-shot experiment, a 54% increase relative to the Orbitrap Eclipse. We further leverage this remarkable sensitivity to profile the target engagement landscape of FDA-approved covalent drugs, including sotorasib and adagrasib. We herein provide a reference for the optimal use of the advanced MS platform.","journal":"Molecular & Cellular Proteomics","year":2025,"id":512853,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9591,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":271814,"name":"Ka Yang","orcid":"0000-0001-8937-7397","position":1,"is_corresponding":false},{"id":144421,"name":"S. Li","orcid":null,"position":2,"is_corresponding":false},{"id":633542,"name":"Martin Zeller","orcid":null,"position":3,"is_corresponding":false},{"id":536092,"name":"Graeme C. McAlister","orcid":null,"position":4,"is_corresponding":false},{"id":1014933,"name":"Hamish Stewart","orcid":"0009-0000-1510-4353","position":5,"is_corresponding":false},{"id":1015702,"name":"Christian Hock","orcid":null,"position":6,"is_corresponding":false},{"id":1014929,"name":"Eugen Damoc","orcid":"0000-0002-4422-2866","position":7,"is_corresponding":false},{"id":247290,"name":"Vlad Zabrouskov","orcid":"0000-0003-3567-9407","position":8,"is_corresponding":false},{"id":107041,"name":"Steven P. Gygi","orcid":"0000-0001-7626-0034","position":9,"is_corresponding":false},{"id":136624,"name":"Joao A. Paulo","orcid":"0000-0002-4291-413X","position":10,"is_corresponding":false},{"id":228749,"name":"Qing Yu","orcid":"0000-0003-0468-5353","position":11,"is_corresponding":false},{"id":109560,"name":"Yuchen He","orcid":"0000-0002-6096-4915","position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-19T02:48:11.233009Z","pmid":"40210101","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}